Inteligencia artificial: La guía completa para principiantes del futuro de la IA

Por John Adamssen

Este es un paquete de 3 libros, que aborda varios subtemas, incluidos, entre otros, estos:



Libro 1: En esta guía, aprenderá sobre todos los conceptos básicos de la inteligencia artificial. Aprenderá qué es, cómo funciona y de dónde viene (o, en otras palabras, cómo comenzó todo).


Aparte de eso, nos sumergiremos en algunos análisis de datos y ejemplos de inteligencia artificial. Cubriremos varios pasos en el proceso analítico y veremos qué se necesita para que la inteligencia artificial sea efectiva.


Por último, pero no menos importante, los problemas de seguridad y privacidad saldrán a la luz, ya que la era de hoy está llena de piratería, espionaje y robo. Por lo tanto, es obligatorio que estos dispositivos y sistemas se mantengan seguros y protegidos.



Libro 2: Muchas personas tienen preguntas sin respuesta sobre inteligencia artificial. Hoy, la mayoría de esas preguntas probablemente serán respondidas. Se abordarán las preocupaciones y se darán ejemplos. Este libro comienza con una sección de preguntas y respuestas sobre inteligencia artificial.


Luego procede a cubrir aplicaciones específicas inteligentes artificialmente, como chatbots y robótica. Estas páginas mostrarán detalles de cosas que desconciertan las mentes de muchas personas. Pero no se quedará en la oscuridad y disfrutará de todos los beneficios de este conocimiento.



Libro 3: ¿Pueden las máquinas escribir libros?

¿Se puede usar la inteligencia artificial para los negocios?

¿Habrá pantallas táctiles o serán reemplazadas por reconocimiento de voz?

¿Qué son los deepfakes?

¿Cómo funcionan los autos autónomos, y van a ser una realidad pronto?


Todas estas preguntas salen a la luz en este breve pero informativo libro sobre inteligencia artificial. La sociedad está cambiando rápidamente debido a los sistemas automatizados que benefician o socavan el estilo de vida, el trabajo y el cerebro de las personas. Hoy exploramos lo que puede deparar ese futuro. También buscaremos opciones para que los civiles en el mundo moderno de hoy se adapten más rápidamente.



No subestimes el aumento de la inteligencia artificial. Comprende el futuro. Comience a leer o escuchar ahora!

• Idioma: Español
• Editorial: Efalon Acies
• Fecha de lanzamiento: 3 ago 2020
• ISBN: 9788835874843

Vista previa del libro

Matter?

Chapter 1: AI History

The term AI was created in 1956, but AI has become more well-known today thanks to increased information volumes, advanced algorithms, and improvements in computing power and storage.

Early A.I. research in the 1950s checked out topics like problem solving and symbolic methods. In the 1960s, the U.S. Department of Defense took interest in this kind of work and started training computer systems to mimic fundamental human thinking. An example would be the following: the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced smart personal assistants in 2003, long before Siri, Alexa or Cortana were household names.

This early work led the way for the automation and official reasoning that we see in computers today, which includes decision support systems and wise search systems that can be created to enhance and augment human abilities.

While Hollywood motion pictures and science fiction novels illustrate AI as human-like robots that take over the world, the current development of AI technologies isn't that scary-- or quite that clever. Rather, Artificial Intelligence has progressed to provide many specific advantages in every industry. Keep reading for contemporary examples of artificial intelligence in healthcare, retail and more.

Why is AI crucial?

AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Rather than automating manual jobs, Artificial Intelligence performs regular, high-volume, electronic tasks reliably and without tiredness. For this type of automation, human inquiry is still vital to set up the system and ask the right questions.

AI adds intelligence to existing items. In many cases, AI will not be sold as an individual application. Rather, products you already use will be enhanced with Artificial Intelligence abilities, just like Siri was added as a feature to a new generation of Apple items. Automation, conversational platforms, bots and smart machines can be combined with big amounts of data to enhance many innovations at home and in the workplace, from security intelligence to investment analysis.

AI adapts through progressive learning algorithms to let the information do the programming. A.I. finds structure and regularities in data so that the algorithm gets an ability: The algorithm ends up being a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what item to suggest next online. And the models adjust when given new data. Back propagation is an Artificial Intelligence technique that permits the model to adjust, through training and added data, when the first answer is not rather right.

AI analyzes more and deeper data using neural networks that have many hidden layers. Building a scams detection system with five hidden layers was practically impossible a few years ago. All that has changed with extraordinary computer power and huge information. You really need lots of data to train deep learning models simply because they learn straight from the data. The more information you can feed them, the more accurate they end up being.

AI achieves unbelievable precision through deep neural networks-- which was previously unrealistic. Here is an example: your interactions with Alexa, Google Search and Google Photos are all based on deep learning-- and they keep getting more accurate the more we use them. In the medical field, A.I. strategies from deep learning, image categorization and thing recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.

AI gets the most out of information. When algorithms are self-learning, the information itself can end up being copyright. The answers are in the information; you just have to apply Artificial Intelligence to get them out. Since the role of the data is now more crucial than ever previously, it can create a competitive advantage. If you have the best data in a competitive market, even if everybody is using comparable strategies, the best information will win.

What are the challenges of using artificial intelligence?

Artificial intelligence is going to change each market, but we need to understand its limits.

The concept constraint of A.I. is that it gains from the data. There's no other method which knowledge can be integrated. That means any inaccuracies in the data will be shown in the results. And any added layers of prediction or analysis have to be added individually.

Today's Artificial Intelligence systems are trained to do a clearly described task. The system that plays poker can't play solitaire or chess. The system that discovers scams cannot drive an automobile or give you legal advice. Actually, an Artificial Intelligence system that finds health care fraud can't properly discover tax scams or service warranty claims fraud.

In other words, these systems are really, extremely specialized. They are focused on a single job and are far from behaving like human beings.

Also, self-learning systems are not independent systems. The imagined A.I. innovations that you see in motion pictures and TV are still science fiction. But computer systems that can penetrate complicated data to learn and perfect specific jobs are becoming quite typical.

Chapter 2: How AI Functions

AI works by combining big quantities of information with fast, iterative processing and smart algorithms, allowing the software to learn automatically from patterns or functions in the information. A.I. is a broad discipline that includes many theories, methods and technologies, and the following major subfields:

Machine learning automates analytical model building. It uses techniques from neural networks, data, operations research and physics to find hidden insights in data without clearly being configured for where to look or what to conclude.

A neural network is a type of machine learning that is made up of interconnected systems (like nerve cells) that processes information by responding to external inputs, communicating info between each unit. The process needs several passes at the data to find connections and obtain meaning from undefined information.

Deep learning uses substantial neural networks with many layers of processing units, making the most of advances in computing power and improved training strategies to learn intricate patterns in large amounts of data. Common applications include image and speech recognition.

Cognitive computing is a subfield of AI that pursues a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate objective is for a machine to imitate human procedures through the capability to translate pictures and speech-- and after that speak coherently in reaction.

Computer system vision counts on pattern recognition and deep learning to recognize what's in a picture or video. When devices can process, examine and understand images, they can capture images or videos in real time and translate their surroundings.

Natural language processing is the ability of computers to analyze, understand and produce human language, including speech. The next stage of NLP is natural language interaction, which enables people to communicate with computers using typical, everyday language to perform jobs.

In addition, some technologies make it possible for and support AI:

Visual processing systems are essential to AI because they offer the heavy compute power that's required for iterative processing. Training neural networks needs huge information plus compute power.

The Internet of Things produces huge quantities of data from linked gadgets, the majority of it unanalyzed. Automating models with AI will allow us to use more of it.

Advanced algorithms are being developed and combined in new ways to analyze more data faster and at several levels. This intelligent processing is key to identifying and forecasting rare events, comprehending intricate systems and optimizing unique circumstances.

APIs, or application programming user interfaces, are portable packages of code that make it possible to add A.I. functionality to existing items and software application packages. They can add image recognition abilities to home security systems and Q&A capabilities that describe information, produce captions and headings, or call out fascinating patterns and insights in data.

In summary, the objective of AI is to offer software application that can